Time series segmentation with shifting means hidden markov models

Analysing Time Series Structure with Hidden Markov Models

This paper considers the problem of extracting the relationships between two time series in a non-linear non-stationary environment with Hidden Markov Models (HMMs). We describe an algorithm which is capable of identifying associations between variables. The method is applied both to synthetic data and real data. We show that HMMs are capable of modelling the oil drilling process and that they ...

Hidden Markov model segmentation of hydrological and enviromental time series

Motivated by Hubert's segmentation procedure [16, 17], we discuss the application of hidden Markov models (HMM) to the segmentation of hydrological and enviromental time series. We use a HMM algorithm which segments time series of several hundred terms in a few seconds and is compu-tationally feasible for even longer time series. The segmentation algorithm computes the Maximum Likelihood segmen...

Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series

Time Series Classiication Using Hidden Markov Models and Neural Networks

| This paper gives a brief overview on statistical classiication of time series using Hidden Markov Models. To overcome some limitations and assumptions made in the Hidden Markov Model framework two successful hybrid extensions using Neural Networks are presented. At rst, a short description of traditional stochastic-based Hidden Markov Model classiiers with diierent kinds of emission probabili...

Inertial Hidden Markov Models: Modeling Change in Multivariate Time Series

Faced with the problem of characterizing systematic changes in multivariate time series in an unsupervised manner, we derive and test two methods of regularizing hidden Markov models for this task. Regularization on state transitions provides smooth transitioning among states, such that the sequences are split into broad, contiguous segments. Our methods are compared with a recent hierarchical ...